1. Field of the Invention
The invention relates to a method for identifying manufacturing anomalies in a manufacturing system comprising a plurality of products which are manufactured with a plurality of manufacturing attributes. More specifically, the invention comprises a method and system for using data-mining techniques to define normality for product performance, and to identify anomalies for manufacturing parameters excluded from the defined normality based on product performance.
2. Description of the Prior Art and Related Information
Today's ever smaller manufactured products may comprise multitudes of components which are assembled in manufacturers' manufacturing facilities. In order to secure steady supplies and obtain lower pricing on components, manufacturers of the products may use a plurality of sources for the components. Such a diversification technique may include receiving different groups, or lots, of the same component from different manufacturers to be included in the same product line or model.
Typically, components are shipped from the component suppliers in lots that have similar component manufacturing parameters. Such parameters may comprise, for example, direct parameters such as the tolerance of a lot of resistors, the threshold of a lot of resistors, the capacitance of a lot of capacitors, the reactance of a log of capacitors; or indirect parameters such as the specific supplier from which a particular lot originated; the shipping method used for transporting the lot to the product manufacturer, or the time of year (date code) that the lot of components were manufactured. Such direct parameters may cause a variation in the quality, reliability or performance of a particular lot of components, thereby causing a variation in the performance of the assembled product. Further, correlation may exist between a variation in performance and indirect parameters. Further, there may be certain interactions between parameters that may lead to variations in performance. Such variations may be positive or negative influences on product performance.
The lots are typically identified by a lot number, called a component identifier herein, which identifies the lot to the product manufacturer and the supplier, the component identifier usually being used for accounting purposes or traceability to manufacturing parameters of the lot. However, especially with small electronic components, the component identifiers themselves may not be imprinted or bar-coded on the components themselves due to practical considerations such as component size, or the extra time and expense in manufacturing that would be required to do so. Therefore, once the components leave their packing materials in which they were shipped, the lot from which those components came from may not be able to be identified.
Certain lots of components may be in some way defective or vary in their performance due to the different manufacturing conditions referenced above, or due to a certain component supplier's neglect or breach in promise to supply a certain quality of components. One solution that has been employed heretofore is for the manufacturer to test statistically significant numbers of components from each lot as they arrive from the individual suppliers. However, with products that are sold for low profit margins, such testing and delay in product assembly has become untenable. More and more product manufacturers have been forced to rely on component suppliers' representations that the supplied components meet the standards set by the product manufactures, or that the supplied components fall within a set standard deviation of performance within each lot. Such reliance has proven inadequate in countless situations, with many product manufactures having their products show variations in performance when it is too late to trace the suspect components back to the lot from which they came.
Still, apart from the components themselves, there may be product manufacturing parameters which may be responsible for variations in performance in products. For example, the temperature or humidity of a factory where the components are assembled into the products may be a contributing factor to product performance. Other product manufacturing parameters such as the assembly line or conveyor belt speed my further affect product performance.
Solutions to the above problems are described in U.S. patent application Ser. No. 09/494,175, filed Jan. 31, 2000, entitled METHOD USING STATISTICALLY ANALYZED PRODUCT TEST DATA TO CONTROL COMPONENT MANUFACTURING PROCESS which is hereby incorporated by reference herein. That application provides for a plurality of analytical tools for identifying correlations between products which show variations in performance and product or component manufacturing parameters. Typical analytical tools which are described in the system of that application include statistical data tools, a data visualization tool, a data-mining tool, an on-line analytical processing (OLAP) tool, an information broadcast tool or some combination thereof. However, typical analytical tools require that the operator of such tools have a working knowledge of which manufacturing parameters may cause product performance to suffer. The programmer is then required to program the operation of a particular analytical tool based on predicted manufacturer parameter anomalies. As a result, unpredictable anomalies may thus go undetected, or may be difficult to pinpoint.
Finally, it is often difficult to ascertain normal operation of a product relative to a manufacturing parameter as compared with anomalous operation. This is especially so with respect to multi-variant manufacturing parameters which may have anomalies in one or more of several performance measure dimensions. For example, one or more configurations of a disk drive head assembly may have anomalous operation with respect to seek time while having a relatively low error rate and normal servo tracking. Given that other head assembly configurations may show anomalous performance in seek time, error rate or servo tracking, defining just what is normal operation can be difficult, much less identifying anomalous operation.
Thus, there is a need for a system and method for defining normal operation in product performance with respect to manufacturing parameters in multiple dimensions. There is further a need for a system and method for identifying unpredicted or unpredictable manufacturing anomalies.